Chasys Draw IES Help:

Processes : Stacking : Super-resolution

What is Super-resolution?

Super-resolution is an image stacking operation that uses several low resolution images to create an enhanced, higher resolution image. It seeks to utilize information embedded in neighboring frames in a sequence to improve the resolution of a given frame. The engine does this by looking for slight motion, and compensating for this to reveal information lost during the sampling process.

Super-resolution works best when aliasing is taking place and the sequence involves slight motion, perhaps over a few pixels (see the image on the left below). Aliasing is necessary because the information gained in the SR image (high-frequency content) was embedded in the low resolution images as aliasing. Images intended for use with this engine should, therefore, not be smoothened in any way.

Animation of Frames of Original 2x Multi-frame Super-resolution

NB: The original sequence was a simulation of a shaking camera, synthesized by shrinking a single image with slightly different offsets.

As you can see, the engine is able to recover extra detail from the images to create a larger high-resolution image.


The “how” bit

You need at least N2 same-size images of the same scene to run this engine, where N is the scaling factor. Since the minimum scaling factor is 2, the absolute minimum number of images is 4. The images need to be aligned; this can be done by either making sure the camera remains perfectly still or by using the Align Images for Stacking feature.

This is the GUI for the Super-resolution feature:

The precision of alignment depends on how good the chosen reference point is, and in rare circumstances Chasys Draw IES will choose a bad point leading to poor alignment. If this happens, just click “Register” to choose a new point and re-align the images afresh.

The GUI offers several algorithms to choose from, as described below:

Algorithm Details

Discrete Pixel Spattering

Unique to Chasys Draw IES, this technique gives the sharpest, best quality results, but only if the data available in the input images is sufficient -- if not, it tends to produce artifacts. It is recommended that you use more than N2 images when using this option.

Blending, Average

This method degrades very gracefully and is tolerant to low image counts, so it can be safely used with fewer than N2 images, albeit at the cost of quality. It produces images that are a little soft, so some sharpening might be required afterwards.

Blending, Median

This method is similar to Average, except that the information is compiled by calculating the average after removing outliers, i.e. it uses the average of the median values.

To maximize on the information available to the super-resolution engine, the input images must not be smoothened in any way. If using video, it should be recorded uncompressed.



Copyright © John Paul Chacha, 2001-2017